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Electromagnetic (EM) sensing is a wide-spread contactless examinationtechnique in science, engineering and military. However, conventional sensingsystems are mostly lack of intelligence, which not only require expensivehardware and complicated computational algorithms, but also pose importantchallenges for advanced in-situ sensing. To address this shortcoming, wepropose the concept of intelligent sensing by designing a programmablemetasurface for data-driven learnable data acquisition, and integrating it intoa data-driven learnable data processing pipeline. This strategy allows to learnan optimal sensing chain in systematic sense of variational autoencoder, i.e.,to jointly learn an optimal measurement strategy along with matching data postprocessing schemes. A three-port deep artificial neural network (ANN) isdesigned to characterize the measurement process, such that an optimalmeasurement strategy is adaptive to the subject of interest by controlling theprogrammable metasurface for manipulating the EM illuminations. We design andfabricate a proof-of-principle sensing system in microwave, and demonstrateexperimentally its significance on the high-quality imaging and high-accuracyobject recognition from a remarkably reduced number of measurements. Wefaithfully expect that the presented methodology will provide us with afundamentally new perspective on the design of intelligent sensingarchitectures at various frequencies, and beyond.

Intelligent Electromagnetic Sensing with Learnable Data Acquisition and Processing